Machine Learning
Game theory, on-line prediction and boosting
COLT '96 Proceedings of the ninth annual conference on Computational learning theory
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Generalization in decision trees and DNF: does size matter?
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Combinations of weak classifiers
IEEE Transactions on Neural Networks
Linear Programming Boosting via Column Generation
Machine Learning
Boosting Methods for Regression
Machine Learning
Sparse Regression Ensembles in Infinite and Finite Hypothesis Spaces
Machine Learning
Logistic Regression, AdaBoost and Bregman Distances
Machine Learning
Constructing Boosting Algorithms from SVMs: An Application to One-Class Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
A geometric approach to leveraging weak learners
Theoretical Computer Science
Looking for lumps: boosting and bagging for density estimation
Computational Statistics & Data Analysis - Nonlinear methods and data mining
Ensembles of Learning Machines
WIRN VIETRI 2002 Proceedings of the 13th Italian Workshop on Neural Nets-Revised Papers
Maximizing the Margin with Boosting
COLT '02 Proceedings of the 15th Annual Conference on Computational Learning Theory
An introduction to boosting and leveraging
Advanced lectures on machine learning
Dimensionality reduction via sparse support vector machines
The Journal of Machine Learning Research
Machine Learning
Boosting as a Regularized Path to a Maximum Margin Classifier
The Journal of Machine Learning Research
The Dynamics of AdaBoost: Cyclic Behavior and Convergence of Margins
The Journal of Machine Learning Research
Statistics and Computing
Neural network ensemble strategies for financial decision applications
Computers and Operations Research
Unifying the error-correcting and output-code AdaBoost within the margin framework
ICML '05 Proceedings of the 22nd international conference on Machine learning
How boosting the margin can also boost classifier complexity
ICML '06 Proceedings of the 23rd international conference on Machine learning
Totally corrective boosting algorithms that maximize the margin
ICML '06 Proceedings of the 23rd international conference on Machine learning
Experiments with AdaBoost.RT, an improved boosting scheme for regression
Neural Computation
Unifying multi-class AdaBoost algorithms with binary base learners under the margin framework
Pattern Recognition Letters
Some Theory for Generalized Boosting Algorithms
The Journal of Machine Learning Research
The Journal of Machine Learning Research
Nonlinear Boosting Projections for Ensemble Construction
The Journal of Machine Learning Research
Computational Statistics & Data Analysis
Increasing the Robustness of Boosting Algorithms within the Linear-programming Framework
Journal of VLSI Signal Processing Systems
Zero-anaphora resolution by learning rich syntactic pattern features
ACM Transactions on Asian Language Information Processing (TALIP)
Computational Statistics & Data Analysis
ACM Transactions on Asian Language Information Processing (TALIP)
Sketching information divergences
Machine Learning
Boosting with incomplete information
Proceedings of the 25th international conference on Machine learning
Support Vector Machinery for Infinite Ensemble Learning
The Journal of Machine Learning Research
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
Nonlinear clustering-based support vector machine for large data sets
Optimization Methods & Software - Mathematical programming in data mining and machine learning
Boosting additive models using component-wise P-Splines
Computational Statistics & Data Analysis
Prototype classification: Insights from machine learning
Neural Computation
Classification of peptide mass fingerprint data by novel no-regret boosting method
Computers in Biology and Medicine
Supervised projection approach for boosting classifiers
Pattern Recognition
Information theoretic regularization for semi-supervised boosting
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Towards a Linear Combination of Dichotomizers by Margin Maximization
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
Least squares support vector machines ensemble models for credit scoring
Expert Systems with Applications: An International Journal
Probabilistic classification vector machines
IEEE Transactions on Neural Networks
Sketching information divergences
COLT'07 Proceedings of the 20th annual conference on Learning theory
Boosting through optimization of margin distributions
IEEE Transactions on Neural Networks
Enhancing the classification accuracy by scatter-search-based ensemble approach
Applied Soft Computing
Probability density estimation with tunable kernels using orthogonal forward regression
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on gait analysis
Approximation stability and boosting
ALT'10 Proceedings of the 21st international conference on Algorithmic learning theory
A Refined Margin Analysis for Boosting Algorithms via Equilibrium Margin
The Journal of Machine Learning Research
Expert Systems with Applications: An International Journal
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Infinite ensemble learning with support vector machines
ECML'05 Proceedings of the 16th European conference on Machine Learning
ECML'05 Proceedings of the 16th European conference on Machine Learning
A primal-dual convergence analysis of boosting
The Journal of Machine Learning Research
What Drives Short Rate Dynamics? A Functional Gradient Descent Approach
Computational Economics
Boosting GARCH and neural networks for the prediction of heteroskedastic time series
Mathematical and Computer Modelling: An International Journal
Margin optimization based pruning for random forest
Neurocomputing
ADANET: inferring gene regulatory networks using ensemble classifiers
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
New machine learning algorithm: random forest
ICICA'12 Proceedings of the Third international conference on Information Computing and Applications
On the doubt about margin explanation of boosting
Artificial Intelligence
Fully corrective boosting with arbitrary loss and regularization
Neural Networks
The rate of convergence of AdaBoost
The Journal of Machine Learning Research
Machine learning-based classifiers ensemble for credit risk assessment
International Journal of Electronic Finance
Model-based boosting in R: a hands-on tutorial using the R package mboost
Computational Statistics
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